1. Field
The present disclosure relates to a system and method that extracts an atrial signal from a surface electrocardiogram, and more particularly, to a system and method that extracts only a signal close to an atrial signal from a multi-lead surface electrocardiogram in which a ventricular signal and an atrial signal measured by a non-invasive method are mixed.
2. Description of the Related Art
FIG. 1 illustrates a typical electrocardiogram waveform. Here, a T wave represents a ventricular repolarization signal occurring after ventricular depolarization (QRS complex), and is used to predict a disease such as myocardinal ischemia, acute myocardinal infarction, myocarditis, pericarditis, ventricular hypertrophy, ventricular arrhythmia, and the like, by observing repetitive changes (alternation phenomenon) in width or height of the T wave every alternate cycle. This ventricular repolarization signal is being widely used because of having a large waveform and being visually distinguishable.
Similar to ventricular repolarization, atrial repolarization occurs after atrial depolarization (P wave). This atrial repolarization signal (Ta) is indicated by a dotted line in FIG. 1. However, as shown in FIG. 1, a Ta wave is difficult to distinguish by eye due to having a small waveform and being hidden by a ventricular depolarization signal.
However, changes in width or height of a Ta wave (alternation phenomenon) may be used as a predictive factor for a stroke, atrial fibrillation, atrial arrhythmia, a heart failure, an ischemic heart disease, and the like, and thus, a method of identifying a Ta wave is required. Generally, to identify a Ta wave, an invasive method is used, but an invasive method has a drawback of a burden for a patient or a complex procedure.
To overcome this drawback, an independent component analysis method generally used for signal separation may be used to measure a surface electrocardiogram signal and extract an atrial signal in a non-invasive manner. The independent component analysis method is a method which estimates signals closest to source signals to find an unmixing matrix able to separate the source signals from observed mixed signals, based on the assumption of mutual independence between the source signals in a state that prior knowledge of the source signals is unknown.
However, in case in which a conventional independent component analysis technique is used, the shortcoming is that it is impossible to understand what each separated signal represents and comparison of each signal is not easy due to different scales for each separated signal. That is, according to a general independent component analysis algorithm, there are problems that identifying an atrial signal among each output signal must rely on an intuitive decision by a physician and very different sizes of each output signal hamper a physician's making an intuitive decision. Also, to obtain only a particular signal, all signals need to be separated according to characteristics of an algorithm, requiring a large amount of computation. For these all reasons, a general independent component analysis algorithm is not suitable for an application field of the present disclosure.
Conventionally, to extract only an atrial signal from a surface electrocardiogram, an average beat subtraction method which estimates an atrial signal by removing a ventricular signal, that is, an ensemble average of a QRST waveform (FIG. 1) from a surface electrocardiogram has been widely used. However, because an average beat subtraction method estimates an atrial signal by subtracting a fixed QRST waveform in each electrocardiogram cycle, there is a limitation of a large residual error occurring when a QRST waveform changes over time.
Recently, to resolve this issue, attempts have been made to estimate an atrial signal by applying a principal component analysis or singular value decomposition technique. Both the two methods may estimate an atrial signal with a smaller residual error than a conventional average beat subtraction method. However, to resolve a signal discontinuity issue raised when finding elements corresponding to an atrial signal from a separated result or reconstructing an atrial signal by mapping these elements, a post-processing process is needed, taking statistical characteristics of a signal into account, and even though post-processing is performed, an error occurring when compensating for discontinuity may distort a P-Ta waveform of a very small size.
More recently, a method of extracting an atrial signal from Holter electrocardiogram recording in a simple manner using an adaptive filter is proposed. Atrial fibrillation may be effectively diagnosed by calculating a delay time of a P wave in an atrial signal extracted through the proposed method and counting the number of occurrences. However, the extracted atrial signal is a signal for atrial fibrillation diagnosis, and is difficult to regard as a signal close to an original atrial signal. Accordingly, the signal may be an index indicating that atrial fibrillation occurred, but has a limitation in extracting information that may be used as a predictive factor.